AI that does real work, safely.
We build assistants, document pipelines and agents that are grounded in your data, measured against real examples and wired into the systems your team already uses.
From promising demo to dependable feature.
A demo takes a weekend; a feature people trust takes evaluation, guardrails and good engineering. We do both halves.
Use-case assessment
We test whether AI genuinely beats a simpler approach for your problem — and tell you plainly if it doesn't.
Retrieval over your data (RAG)
Search and Q&A across documents, wikis and databases with citations, access controls and freshness built in.
Agents & tool use
Models that call your APIs to look things up, draft, file or route work — with human approval where it matters.
Document processing
Extracting fields from invoices, contracts and forms into structured data, with confidence scores and review queues.
Evaluation & guardrails
Test sets built from real examples, automatic scoring, red-teaming and limits on what the system can see and do.
Classic machine learning
Forecasting, classification and computer vision models where they are a better fit than a language model.
What you walk away with.
Not just a prototype: a measured, monitored system your team understands.
- Use-case and feasibility report
- Working prototype on your data
- Evaluation set and quality scorecard
- Production API or in-app feature
- Prompt, model and retrieval configuration in code
- Access controls and audit logging
- Cost and latency monitoring
- Model-risk and data-flow documentation
Where it pays off.
Illustrative examples of AI that tends to pay for itself quickly when it's built carefully.
Ask-your-documents assistant
Staff ask questions in plain English and get answers drawn from policies, contracts or manuals — with links to the exact page.
Inbox and ticket triage
Incoming emails or tickets classified, summarised and routed, with suggested replies a person can approve in one click.
Invoice and form extraction
Supplier invoices or application forms read automatically into your finance or CRM system, flagging anything unusual.
Product search and recommendations
Semantic search that understands what customers mean, not just the words they type.
How an AI project runs.
Assess
Pick one high-value use case, gather real examples and define what 'good' looks like in measurable terms.
→ success criteriaPrototype
A working version on a sample of your data, scored against the evaluation set so progress is visible.
→ scored prototypeHarden
Guardrails, permissions, fallbacks, monitoring and cost controls; integration into your product or workflow.
→ production-readyOperate
Track quality and cost in production, refresh evaluation sets and improve as models and data change.
→ measured in useTools we reach for.
We stay model-agnostic, so you can switch providers as prices and capabilities change.
Models
- Claude
- OpenAI GPT
- Gemini
- Open-weight models
- Embeddings
Retrieval & orchestration
- pgvector
- Qdrant
- Elasticsearch
- LangGraph
- Model Context Protocol
ML & evaluation
- Python
- PyTorch
- scikit-learn
- Evaluation harnesses
- OpenTelemetry
Is our data used to train the AI models?
No. We use providers and settings where your data isn't used for training, keep documents in your own storage, and design access controls so the assistant only sees what the user asking is allowed to see.
How do you stop the AI making things up?
By grounding answers in retrieved sources, showing citations, refusing when the sources don't contain an answer, and measuring accuracy against an evaluation set before and after launch. No system is perfect, so we also design for human review where errors are costly.
Which model do you use?
Whichever performs best on your evaluation set for the right cost and latency. We keep the model choice behind a clean interface so it can be changed later.
How much does an AI project cost to run?
It depends on volume and model choice. We estimate running costs during the prototype, add caching and smaller models where quality allows, and put cost monitoring in place before launch.
Have a task AI could take off your plate?
Describe the workflow and the documents or data involved. We'll tell you honestly whether AI is the right tool — and what it would take.